Data engineering is becoming increasingly popular, and aspiring data engineers want to know what exactly the profession involves. It’s essential to understand the tools and technologies that data engineers use. This will help you find out what’s expected of you and learn the necessary skills.
The tools data engineers use are programming languages like Python and Scala, along with packages like Spark, NumPy, and Play. They also use data warehousing technologies, shell languages, cloud computing solutions, big data technologies, visualization and reporting tools, and SQL.
We know, just giving you a list of tools isn’t very helpful. Read on to learn what these tools and technologies are and how data engineers use them in their day-to-day tasks.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Skills Do Data Engineers Need?
Data engineers use a variety of tools as part of their job. They need to extract data from multiple sources and optimize it for analysis. Some of their routine tasks include building data pipelines, managing databases on the cloud, and using processing engines to manipulate data.
Following are the must-have skills required for becoming a data engineer. There are other things as well, but they’re not absolutely central to data engineering, so we haven’t included them here.
Programming
Data engineering is a highly technical job, and it requires strong coding skills. The better programmer you are, the more competent data engineer you become. Knowing how to code is a vital skill for any data engineer around the globe.
Python is the most common programming language for data engineering. According to Cloud Academy, it’s the second most sought skill for this job. This is because Python is flexible, simple, and combines well with other languages.
In bigger companies, Python is usually used together with other programming languages such as Scala, R, and Java. This means knowing more than one language can give your career a boost. However, most entry-level data engineering jobs require you to be proficient in at least one of these programming languages, with Python being the most popular option.
Apart from being a skilled programmer, data engineers must also know how to use the various frameworks and libraries that come with the language.
If you want to learn more about the importance of coding skills in data engineering, read this article: Does Data Engineering Require Coding? and you can check out the post on Data engineering and Python to learn more about the role of Python specifically.
Database Management
Working with databases and manipulating data is at the core of data engineering. Therefore, data management is a vital skill if you want to become a data engineer.
SQL is the standard language for creating and managing relational database systems. It is used extensively in data manipulation and management. So if you want to become a data engineer, SQL is a critical skill to learn. You need an in-depth knowledge of SQL as, according to Cloud Academy, SQL is the most sought skill for this job.
NoSQL databases like MongoDB and Couchbase are also popular. They are different, and in some cases, better than SQL databases. So data engineers should know how to handle both SQL and NoSQL databases.
Data Warehousing
Since we are generating quintillions of data every day, data engineers need to know how to store this data securely before so that they can work on it. A data warehouse is used to store and analyze large amounts of data from various sources. It connects several sources of data, reducing the stress on the production system. You can quickly access critical information from several sources in a single place.
Data engineers must be skilled at using data warehousing solutions like Redshift and Panoply. SQL is the standard language when it comes to data warehouses like Amazon Redshift. Data engineers are required to run complex queries on structured data.
Cloud Computing
Most data infrastructures are built on cloud platforms these days. So data engineering and cloud computing essentially go hand in hand. Data engineers deal with large amounts of complex datasets, and cloud platforms offer a convenient way of accessing and manipulating this data.
Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS) even offer certifications to prove that you know how to work with their technologies. Having one of these certificates can significantly boost your career since cloud platforms are crucial for data engineers.
We’ve discussed everything about Google Cloud Data Engineering Certification in a separate article. You can read it here: Is Google Data Engineer Certification Worth It?
ETL Tools
Extract, Transform, Load (ETL) is a category of tools and technologies used to move data between systems. Data engineers use them to extract data from various sources, transform or cleanse them in different ways to make it suitable for analysis, and then store it into the destination system. They build what’s known as a data pipeline to perform these tasks automatically.
For example, an ETL process may look like this:
- Extract all entries from the address column of this database.
- Identify and separate house numbers, street names, and zip codes.
- Load this optimized data into a destination system to analyze it at the zip code level.
Apache Spark and Hadoop
These two programs are crucial for data engineering. As a data engineer, you will be using them almost every day. Spark is an open-source data processing engine that can process large datasets quickly. Apache Hadoop is another software library that does the same job.
The primary difference between the two programs is that Spark supports stream processing, allowing for continuous data input and output. On the contrary, Hadoop uses batch processing, gathering data in batches and processing it all at once.
Operating Systems
Data engineers should have intimate knowledge of operating systems like Linux, UNIX, and Solaris. Many of the integral data engineering tools are based on these systems. Microsoft Windows or Mac OS don’t offer the same functionality and root access to hardware.
You can find many free and paid courses online for learning about different operating systems. For starters, here’s a Coursera course on how Linux works in the enterprise. It covers the basics of the Linux operating system and prepares you for the real world.
Machine Learning
Machine learning is not the core of data engineering; it’s mostly a data scientist’s focus. Data engineers don’t build machine learning models, nor do they feed data into the ML models designed by data scientists. The only thing a data engineer cares about is how to best optimize the datasets for data scientists and business intelligence analysts.
However, data engineers should still be familiar with the basics of machine learning algorithms and data structures. Since they closely work with data scientists and machine learning engineers, knowing the fundamentals of machine learning helps them understand their needs and collaborate with them better.
We’ve taken an in-depth look at data engineering and machine learning in another article, which you can read here: Do Data Engineers Do Machine Learning?
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
Data engineering is a technical job that requires you to be skilled at using several tools and technologies. The primary skill that any data engineer has is coding knowledge. Every data engineer is an expert coder.
They also need to know how to handle SQL and NoSQL databases and store large amounts of data safely using data warehousing solutions. ETL tools are also indispensable for data engineers as cleaning and transferring data is a core part of their job.
Data engineers need intimate knowledge of programs like Apache Spark and Hadoop and are familiar with operating systems like Linux.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.
